Untitled Quiz

Language models built using statistical learning methods from the 1990s.

A principle used in SLMs to predict the next word based on recent words.

Statistical language models with a fixed context length (n).

A type of n-gram model using a context of 2 preceding words (n=2).

A type of n-gram model using a context of 3 preceding words (n=3).

A complex skill that machines struggle with without advanced AI algorithms.

A field where SLMs can improve task performance.

A field where SLMs can enhance task performance ,

Large language models are pre-trained models with a significant number of parameters, typically in the tens or hundreds of billions.

A crucial step for LLM training, involving training the model on a large text dataset to learn general language patterns.

Adjusting a pre-trained LLM to perform specific tasks or match a certain style.

Using a pre-trained or tuned LLM to fulfill specific tasks or applications. Using a model to do something.

Assessing the capabilities and limitations of an LLM.

An application using an LLM to conduct conversations in a human-like way.

Skills or capabilities that a language model displays unexpectedly and which were not explicitly programmed.

A measure of the difference between two probability distributions, used in evaluating the performance of language models.

The inherent uncertainty in the data itself; the entropy of the true data distribution.

The remaining difference between the model's predictions and the true data distribution.

Loss function used to evaluate the performance of a language model

Relationship between model performance and factors such as data size, model size, and training compute.

A specific example of scaling law, relating model size, data size, and compute.

Cross entropy loss related to data D, denoted in nats.

Cross entropy loss related to data C in nats.

GPT-2, while intended as an unsupervised multitask learner, performs less well than supervised tuning methods.

GPT-2's performance is improved by adjusting it for specific tasks, especially dialogue.

GPT-2's relatively small size makes it often fine-tuned for specific tasks.

The massive dataset used to train foundation models for tasks.

The computational resources needed to train and run LLMs.

LLMs whose model checkpoints are publicly accessible.

A method of fine-tuning large language models using instruction.

A method of representing words as vectors, where similar words have similar vectors in a high-dimensional space.

A function that predicts the next word in a sequence based on the context of preceding words.

Combined features of the context surrounding a word, used for predicting the next word.

A method using interconnected nodes to learn and make predictions.

A complete system that learns from the data and solves a problem without intermediary steps.

A statistical model that predicts the probability of a sequence of words.

A more complex language model handling vast amounts of text data for more sophisticated processing, like generation.

Scientific articles submitted to the arXiv preprint server.

A running total of something over a period of time. Tracking an increase.

Important words or short phrases to describe the topic of a text.

An institution specializing in Artificial Intelligence.

An academic department focused on information technology.

A university in China.

A research department in Montreal University.

Word representations as vectors in a high-dimensional space.

GPT-4 demonstrates significant improvement over GPT-3.5 in handling various tasks like complex problem-solving. It showcases a marked leap in performance across numerous evaluation benchmarks.

GPT-4 was assessed using qualitative tests involving human-generated problems that covered a wide range of complex tasks, including those posing challenges like reasoning, knowledge, planning, and creativity.

GPT-4 leverages a method known as Reinforcement Learning from Human Feedback (RLHF) to align its responses with human preferences. This helps it generate safer outputs, especially in response to malicious or provocative queries.

GPT-4 incorporates safety mechanisms like "red teaming" to reduce the risk of generating harmful or toxic content. This emphasizes the importance of responsible development and deployment of LLMs.

GPT-4 utilizes "predictable scaling" to accurately forecast its final performance based on early training observations. This helps ensure efficient development and deployment of large language models.

InstructGPT, a predecessor to GPT-4, utilizes a three-stage reinforcement learning from human feedback (RLHF) algorithm. This approach fine-tunes language models to better understand and follow human instructions.

GPT-4 benefits from a process called "instruction tuning", which involves supervised fine-tuning on human demonstrations. This helps improve the model's ability to follow instructions effectively.

The RLHF algorithm helps minimize the generation of harmful or toxic content by LLMs. This is fundamental for the safe and practical deployment of LLMs.